Stop Cargo Theft: AI Security Meets New Federal Bill

Cargo theft is no longer a “cut the seal and disappear” problem. It’s a data problem.

At a recent House Judiciary hearing, industry leaders described a surge in digitally-driven “strategic theft”—where criminal groups manipulate shipment information, impersonate legitimate partners, and redirect loads while they’re already moving. The American Transportation Research Institute (ATRI) estimates cargo theft costs motor carriers $1.83B to $6.56B annually, with average losses of $29,108 per incident for motor carriers and $95,351 for logistics companies. ATA also cited strategic theft rising 1,500% since Q1 2021, with average value per theft over $200,000.

That’s why trucking and retail are pushing Congress to pass the Combating Organized Retail Crime Act (CORCA). And it’s why this moment matters for the AI in Supply Chain & Procurement conversation: you can’t police your way out of an identity-and-data-driven crime wave without better detection, better coordination, and faster decision-making—three areas where AI in transportation and logistics is already proving its value.

Why cargo theft is surging (and why “more locks” won’t fix it)

Cargo theft is surging because criminals attack information flows, not just physical freight. When a bad actor can edit a bill of lading, spoof an email domain, or exploit a weak carrier onboarding workflow, they don’t need to break into a trailer. They just need you to hand the load to the wrong “carrier.”

Industry testimony described transnational groups operating overseas with the ability to:

• Infiltrate communications between brokers, shippers, and carriers
• Pose as legitimate carriers using stolen identities
• Redirect freight mid-route by manipulating shipment instructions
• Convert stolen goods to cash through online marketplaces and fraud rails

Here’s the stance I’ll take: most cargo theft prevention programs still over-invest in visible security and under-invest in data integrity. High-security padlocks and yard cameras help, but they don’t stop a fraudulent dispatch, a doctored pickup number, or a “carrier” that shouldn’t have been onboarded in the first place.

The holiday effect makes everything worse

November and December are prime time for organized theft and fraud. Volumes spike, peak-season staffing is stretched, exception queues grow, and teams accept more “quick changes” to keep freight moving. Retailers also sell more gift cards, which creates a parallel fraud economy that can fund broader criminal activity.

That’s not a morality tale—it’s just incentives. Criminal networks go where process strain is highest.

What CORCA changes—and what it doesn’t

CORCA aims to create a unified federal response to organized retail crime and cargo theft. The big idea is coordination: state and local agencies can chase individual incidents, but transnational networks require national-level information sharing, trend detection, and cross-jurisdiction investigation.

Based on the hearing summary and bill provisions discussed, CORCA would:

• Create an Organized Retail and Supply Chain Crime Coordination Center within ICE to align federal, state, and local investigations and share intelligence.
• Expand money laundering statutes to include illicit gift card use.
• Lower the federal threshold for interstate transportation of stolen property to $5,000 aggregate value over any 12-month period.
• Increase oversight of digital marketplaces that enable resale of stolen goods.

CORCA is directionally right because it focuses on the real operating model: organized groups, fast monetization, and digital enablement.

But legislation doesn’t magically upgrade day-to-day shipper and carrier workflows. Even if CORCA passes, companies will still need to answer practical questions:

• Which loads are most likely to be targeted?
• Which carriers, email domains, phone numbers, or bank accounts look “off” today?
• Which route changes are legitimate vs. a hijack attempt?
• How quickly can we detect and stop a fraudulent pickup before freight disappears?

Those are operational questions. And operational questions are where AI risk management in logistics earns its keep.

Where AI actually helps: from visibility to decision-grade security

AI improves logistics security by detecting patterns humans can’t reliably spot at scale. The goal isn’t to replace your team’s judgment—it’s to shrink the time between “something’s weird” and “stop the load.”

Below are four high-value AI applications that connect directly to the theft patterns described in the hearing.

1) Predictive theft risk scoring for loads, lanes, and facilities

Answer first: AI can assign a theft-risk score to each shipment so teams prioritize the right controls on the right freight.

A good risk model blends:

• Lane history (hotspots, recurring incident corridors)
• Commodity attractiveness (electronics, apparel, alcohol, OTC meds)
• Shipment properties (high value, easy to fence, low traceability)
• Timing signals (weekends, holiday peaks, late-night pickups)
• Operational “friction” (last-minute carrier swaps, reroutes, appointment changes)

This matters because you don’t need maximum security on every load. You need adaptive security—strongest where risk is highest.

Practical outcome: you route high-risk loads through more secure yards, require stricter pickup authentication, add geofencing alerts, or mandate team-based approval for any in-transit changes.

2) Identity and onboarding anomaly detection (the quiet epicenter)

Answer first: Many strategic theft events begin with carrier identity fraud; AI can flag suspicious onboarding behavior before a tender is accepted.

In brokerage and procurement teams, carrier onboarding often happens fast, under pressure, and across multiple systems. That’s exactly what criminals exploit.

AI can spot anomalies such as:

• Newly created domains mimicking known carriers
• Phone numbers that appear across multiple “carriers”
• Bank accounts reused across unrelated entities
• Login patterns inconsistent with a claimed location
• Sudden changes to payment instructions or contact details

This is AI for supplier risk applied to carriers and logistics partners. If your procurement program already uses AI to score supplier risk, extending it to transportation suppliers is a natural next step.

3) Real-time monitoring that’s more than “dots on a map”

Answer first: AI-based monitoring detects behavioral anomalies in transit, not just location.

Traditional tracking tells you where the truck is. That’s table stakes. Modern cargo theft often involves subtle sequence changes—stops that “look normal” until you compare them to patterns.

AI can trigger alerts on:

• Route deviation that correlates with known theft clusters
• Unscheduled dwell time at unsecured locations
• Trailer separation events or sensor silence
• Unusual ping frequency changes (signal jamming patterns)
• “Perfectly timed” appointment changes that match previous fraud plays

The best teams treat these as triage signals, not automatic accusations. An alert should produce a clear next action: verify pickup authority, call a verified number (not the one in the email), require a photo at the dock, or initiate escalation.

4) Fraud and resale network intelligence

Answer first: AI helps connect theft incidents to monetization paths—gift cards, marketplaces, and repeated actor fingerprints.

The hearing highlighted gift card fraud at scale (with estimates of over $1B in losses over two years in that category) and the role of secondary markets. Once stolen goods convert to cash, the network funds more activity.

AI can help by linking:

• Seller accounts and listing patterns across marketplaces
• Repeated product mixes and pricing anomalies
• Common shipping addresses, drop locations, and return behaviors
• Payment instrument reuse and velocity patterns

Even if your organization can’t directly police marketplaces, you can use this intelligence to:

• Adjust packaging and labeling to reduce resale value
• Modify release policies for high-fraud SKUs
• Improve loss recovery documentation and case building

A practical playbook: 7 controls to implement in the next 60 days

Answer first: You can reduce strategic theft risk quickly by tightening identity, change control, and exception handling—then layering AI to scale those decisions.

Here’s what I’d prioritize before “buying another dashboard.”

1. Lock down change control for carrier assignment, pickup number release, and in-transit reroutes (two-person approval for high-risk loads).
2. Use verified contact rules: call-back to a trusted number on file, not the number provided in a last-minute email.
3. Add structured reason codes for every exception (reroute, reconsign, appointment change). Models learn from clean data.
4. Segment freight by risk tier (A/B/C). Apply strictest authentication to tier A.
5. Harden carrier onboarding with automated identity checks and anomaly flags (domain age, bank validation, address consistency).
6. Train dispatch and dock staff on “strategic theft scripts”: what fraud attempts sound like, what data criminals request, what “urgency” patterns look like.
7. Build an escalation runbook so alerts produce action in minutes, not hours (who calls whom, what gets frozen, when law enforcement is notified).

AI makes these controls easier to operate at scale, but the controls themselves matter. A model can’t save a process that allows unverified changes by default.

What procurement and supply chain leaders should ask vendors (and their own teams)

Answer first: The best AI security tools don’t just detect anomalies; they help you make defensible decisions and reduce false alarms.

If you’re evaluating AI in supply chain security—whether through a TMS add-on, a visibility platform, or a risk tool—ask these questions:

• What’s the model trained on? Lane history, sensor streams, exception logs, claims data?
• How does it handle concept drift? Criminal tactics change fast; models must retrain.
• What’s the workflow after an alert? If it doesn’t integrate into dispatch, it won’t get used.
• Can we tune thresholds by risk tier? High-value loads need higher sensitivity.
• How do you prevent alert fatigue? Precision matters more than volume.
• Can we audit decisions? You’ll want explainability for internal reviews and insurers.

A simple litmus test I use: if the tool can’t tell you why a shipment is flagged in plain language, your ops team will ignore it under peak pressure.

The bigger point: CORCA + AI is the real “federal fabric”

CORCA’s promise is coordination—sharing intelligence, aligning agencies, and targeting the financial and digital rails that make organized theft profitable. That’s necessary.

But companies still live in the hour-by-hour reality of tenders, pickups, appointment changes, and exception queues. AI in supply chain & procurement is how you turn raw operational noise into prioritized risk decisions—before a bad handoff becomes a six-figure loss.

If you’re building a 2026 roadmap, I’d treat cargo theft the same way you treat demand volatility and supplier disruption: a forecastable risk with measurable controls. The question isn’t whether criminals will try. The question is whether your systems and teams will notice fast enough.

If you want to pressure-test your current approach, start with one lane, one facility, and one commodity group. Put a theft-risk score in place, tighten change control, and measure outcomes for 60 days. Then scale what works.

What would happen to your on-time performance, claims ratio, and customer experience if you could stop just one strategic theft attempt each month—before the truck leaves the dock?